Overview

Dataset statistics

Number of variables18
Number of observations4000
Missing cells7345
Missing cells (%)10.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory550.9 KiB
Average record size in memory141.0 B

Variable types

Numeric10
Categorical7
DateTime1

Warnings

prev_call_duration is highly correlated with subs_depositHigh correlation
subs_deposit is highly correlated with prev_call_durationHigh correlation
contact_month is highly correlated with contact_weekHigh correlation
contact_week is highly correlated with contact_monthHigh correlation
prev_call_duration is highly correlated with subs_depositHigh correlation
subs_deposit is highly correlated with prev_call_durationHigh correlation
contact_month is highly correlated with contact_weekHigh correlation
contact_week is highly correlated with contact_monthHigh correlation
contact_month is highly correlated with contact_weekHigh correlation
contact_week is highly correlated with contact_monthHigh correlation
contact_weekday is highly correlated with contact_date and 4 other fieldsHigh correlation
subs_deposit is highly correlated with prev_call_duration and 2 other fieldsHigh correlation
prev_call_duration is highly correlated with subs_depositHigh correlation
cpi is highly correlated with subs_deposit and 4 other fieldsHigh correlation
contact_date is highly correlated with contact_weekday and 5 other fieldsHigh correlation
job is highly correlated with educationHigh correlation
poutcome is highly correlated with subs_depositHigh correlation
education is highly correlated with jobHigh correlation
contact_day is highly correlated with contact_weekday and 4 other fieldsHigh correlation
contact_month is highly correlated with contact_weekday and 3 other fieldsHigh correlation
client_id is highly correlated with contact_weekday and 5 other fieldsHigh correlation
contact_week is highly correlated with contact_weekday and 5 other fieldsHigh correlation
education has 176 (4.4%) missing values Missing
has_housing_loan has 92 (2.3%) missing values Missing
has_personal_loan has 92 (2.3%) missing values Missing
prev_call_duration has 79 (2.0%) missing values Missing
days_since_last_call has 3614 (90.3%) missing values Missing
poutcome has 3219 (80.5%) missing values Missing
cpi has 65 (1.6%) missing values Missing
client_id has unique values Unique
num_contacts_prev has 3219 (80.5%) zeros Zeros

Reproduction

Analysis started2022-04-14 00:16:43.198042
Analysis finished2022-04-14 00:23:08.952349
Duration6 minutes and 25.75 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

client_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct4000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22430.64275
Minimum17
Maximum41186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2022-04-14T10:23:09.019003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile2543.9
Q112408.25
median23336.5
Q332990
95-th percentile39701.3
Maximum41186
Range41169
Interquartile range (IQR)20581.75

Descriptive statistics

Standard deviation12052.91754
Coefficient of variation (CV)0.5373416034
Kurtosis-1.20571
Mean22430.64275
Median Absolute Deviation (MAD)10169
Skewness-0.1650511913
Sum89722571
Variance145272821.2
MonotonicityNot monotonic
2022-04-14T10:23:09.131251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
410201
 
< 0.1%
15271
 
< 0.1%
228191
 
< 0.1%
276001
 
< 0.1%
372451
 
< 0.1%
226281
 
< 0.1%
287591
 
< 0.1%
330351
 
< 0.1%
220121
 
< 0.1%
357631
 
< 0.1%
Other values (3990)3990
99.8%
ValueCountFrequency (%)
171
< 0.1%
531
< 0.1%
681
< 0.1%
881
< 0.1%
1011
< 0.1%
1151
< 0.1%
1391
< 0.1%
1801
< 0.1%
1921
< 0.1%
2381
< 0.1%
ValueCountFrequency (%)
411861
< 0.1%
411851
< 0.1%
411811
< 0.1%
411671
< 0.1%
411631
< 0.1%
411481
< 0.1%
411331
< 0.1%
411311
< 0.1%
411181
< 0.1%
411031
< 0.1%

age_bracket
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.4 KiB
2
2161 
3
1544 
1
 
148
4
 
147

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row3
4th row2
5th row1

Common Values

ValueCountFrequency (%)
22161
54.0%
31544
38.6%
1148
 
3.7%
4147
 
3.7%

Length

2022-04-14T10:23:09.332302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-14T10:23:09.392699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
22161
54.0%
31544
38.6%
1148
 
3.7%
4147
 
3.7%

Most occurring characters

ValueCountFrequency (%)
22161
54.0%
31544
38.6%
1148
 
3.7%
4147
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
22161
54.0%
31544
38.6%
1148
 
3.7%
4147
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Common4000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
22161
54.0%
31544
38.6%
1148
 
3.7%
4147
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22161
54.0%
31544
38.6%
1148
 
3.7%
4147
 
3.7%

job
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.37675
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2022-04-14T10:23:09.444916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.309684632
Coefficient of variation (CV)0.6839963372
Kurtosis-1.353254455
Mean3.37675
Median Absolute Deviation (MAD)1
Skewness0.4239568658
Sum13507
Variance5.334643098
MonotonicityNot monotonic
2022-04-14T10:23:09.515196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
11366
34.2%
7769
19.2%
2640
16.0%
5503
 
12.6%
4455
 
11.4%
3153
 
3.8%
6114
 
2.9%
ValueCountFrequency (%)
11366
34.2%
2640
16.0%
3153
 
3.8%
4455
 
11.4%
5503
 
12.6%
6114
 
2.9%
7769
19.2%
ValueCountFrequency (%)
7769
19.2%
6114
 
2.9%
5503
 
12.6%
4455
 
11.4%
3153
 
3.8%
2640
16.0%
11366
34.2%

marital
Categorical

Distinct3
Distinct (%)0.1%
Missing8
Missing (%)0.2%
Memory size31.4 KiB
1.0
2374 
2.0
1176 
3.0
442 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11976
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.02374
59.4%
2.01176
29.4%
3.0442
 
11.1%
(Missing)8
 
0.2%

Length

2022-04-14T10:23:09.692653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-14T10:23:09.751938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.02374
59.5%
2.01176
29.5%
3.0442
 
11.1%

Most occurring characters

ValueCountFrequency (%)
.3992
33.3%
03992
33.3%
12374
19.8%
21176
 
9.8%
3442
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7984
66.7%
Other Punctuation3992
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03992
50.0%
12374
29.7%
21176
 
14.7%
3442
 
5.5%
Other Punctuation
ValueCountFrequency (%)
.3992
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11976
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.3992
33.3%
03992
33.3%
12374
19.8%
21176
 
9.8%
3442
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII11976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.3992
33.3%
03992
33.3%
12374
19.8%
21176
 
9.8%
3442
 
3.7%

education
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.1%
Missing176
Missing (%)4.4%
Memory size31.4 KiB
1.0
1274 
2.0
1114 
3.0
908 
4.0
524 
6.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11472
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01274
31.9%
2.01114
27.9%
3.0908
22.7%
4.0524
13.1%
6.04
 
0.1%
(Missing)176
 
4.4%

Length

2022-04-14T10:23:09.903574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-14T10:23:09.963984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.01274
33.3%
2.01114
29.1%
3.0908
23.7%
4.0524
13.7%
6.04
 
0.1%

Most occurring characters

ValueCountFrequency (%)
.3824
33.3%
03824
33.3%
11274
 
11.1%
21114
 
9.7%
3908
 
7.9%
4524
 
4.6%
64
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7648
66.7%
Other Punctuation3824
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03824
50.0%
11274
 
16.7%
21114
 
14.6%
3908
 
11.9%
4524
 
6.9%
64
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.3824
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.3824
33.3%
03824
33.3%
11274
 
11.1%
21114
 
9.7%
3908
 
7.9%
4524
 
4.6%
64
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.3824
33.3%
03824
33.3%
11274
 
11.1%
21114
 
9.7%
3908
 
7.9%
4524
 
4.6%
64
 
< 0.1%

has_housing_loan
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing92
Missing (%)2.3%
Memory size31.4 KiB
1.0
2115 
0.0
1793 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11724
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.02115
52.9%
0.01793
44.8%
(Missing)92
 
2.3%

Length

2022-04-14T10:23:10.119014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-14T10:23:10.180849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.02115
54.1%
0.01793
45.9%

Most occurring characters

ValueCountFrequency (%)
05701
48.6%
.3908
33.3%
12115
 
18.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7816
66.7%
Other Punctuation3908
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05701
72.9%
12115
 
27.1%
Other Punctuation
ValueCountFrequency (%)
.3908
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05701
48.6%
.3908
33.3%
12115
 
18.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05701
48.6%
.3908
33.3%
12115
 
18.0%

has_personal_loan
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing92
Missing (%)2.3%
Memory size31.4 KiB
0.0
3335 
1.0
573 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11724
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03335
83.4%
1.0573
 
14.3%
(Missing)92
 
2.3%

Length

2022-04-14T10:23:10.319456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-14T10:23:10.376169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.03335
85.3%
1.0573
 
14.7%

Most occurring characters

ValueCountFrequency (%)
07243
61.8%
.3908
33.3%
1573
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7816
66.7%
Other Punctuation3908
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07243
92.7%
1573
 
7.3%
Other Punctuation
ValueCountFrequency (%)
.3908
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
07243
61.8%
.3908
33.3%
1573
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII11724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07243
61.8%
.3908
33.3%
1573
 
4.9%

prev_call_duration
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct958
Distinct (%)24.4%
Missing79
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean325.2152512
Minimum2
Maximum1337
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2022-04-14T10:23:10.444395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile50
Q1128
median231
Q3439
95-th percentile930
Maximum1337
Range1335
Interquartile range (IQR)311

Descriptive statistics

Standard deviation274.3571105
Coefficient of variation (CV)0.8436169873
Kurtosis1.448722397
Mean325.2152512
Median Absolute Deviation (MAD)128
Skewness1.392681103
Sum1275169
Variance75271.82406
MonotonicityNot monotonic
2022-04-14T10:23:10.562847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7220
 
0.5%
16419
 
0.5%
13119
 
0.5%
15919
 
0.5%
16519
 
0.5%
15718
 
0.4%
7617
 
0.4%
15617
 
0.4%
18716
 
0.4%
16116
 
0.4%
Other values (948)3741
93.5%
(Missing)79
 
2.0%
ValueCountFrequency (%)
21
 
< 0.1%
31
 
< 0.1%
44
0.1%
52
 
0.1%
66
0.1%
75
0.1%
83
0.1%
91
 
< 0.1%
104
0.1%
113
0.1%
ValueCountFrequency (%)
13371
< 0.1%
13361
< 0.1%
13292
0.1%
13281
< 0.1%
13271
< 0.1%
13212
0.1%
13191
< 0.1%
13171
< 0.1%
13131
< 0.1%
13091
< 0.1%

days_since_last_call
Real number (ℝ≥0)

MISSING

Distinct23
Distinct (%)6.0%
Missing3614
Missing (%)90.3%
Infinite0
Infinite (%)0.0%
Mean5.743523316
Minimum0
Maximum27
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2022-04-14T10:23:10.664329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median6
Q36
95-th percentile13
Maximum27
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.782258244
Coefficient of variation (CV)0.6585257926
Kurtosis6.758593188
Mean5.743523316
Median Absolute Deviation (MAD)3
Skewness2.100211124
Sum2217
Variance14.30547742
MonotonicityNot monotonic
2022-04-14T10:23:10.754078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
3119
 
3.0%
6118
 
2.9%
427
 
0.7%
516
 
0.4%
214
 
0.4%
1013
 
0.3%
712
 
0.3%
129
 
0.2%
119
 
0.2%
99
 
0.2%
Other values (13)40
 
1.0%
(Missing)3614
90.3%
ValueCountFrequency (%)
05
 
0.1%
16
 
0.1%
214
 
0.4%
3119
3.0%
427
 
0.7%
516
 
0.4%
6118
2.9%
712
 
0.3%
85
 
0.1%
99
 
0.2%
ValueCountFrequency (%)
271
 
< 0.1%
261
 
< 0.1%
251
 
< 0.1%
221
 
< 0.1%
181
 
< 0.1%
171
 
< 0.1%
163
 
0.1%
153
 
0.1%
144
0.1%
138
0.2%

num_contacts_prev
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.272
Minimum0
Maximum6
Zeros3219
Zeros (%)80.5%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2022-04-14T10:23:10.834637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.643132494
Coefficient of variation (CV)2.364457699
Kurtosis11.57486418
Mean0.272
Median Absolute Deviation (MAD)0
Skewness3.035775148
Sum1088
Variance0.4136194049
MonotonicityNot monotonic
2022-04-14T10:23:10.905600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
03219
80.5%
1567
 
14.2%
2144
 
3.6%
354
 
1.4%
410
 
0.2%
55
 
0.1%
61
 
< 0.1%
ValueCountFrequency (%)
03219
80.5%
1567
 
14.2%
2144
 
3.6%
354
 
1.4%
410
 
0.2%
55
 
0.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
55
 
0.1%
410
 
0.2%
354
 
1.4%
2144
 
3.6%
1567
 
14.2%
03219
80.5%

poutcome
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.3%
Missing3219
Missing (%)80.5%
Memory size31.4 KiB
0.0
419 
1.0
362 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2343
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0419
 
10.5%
1.0362
 
9.0%
(Missing)3219
80.5%

Length

2022-04-14T10:23:11.075264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-14T10:23:11.132466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0419
53.6%
1.0362
46.4%

Most occurring characters

ValueCountFrequency (%)
01200
51.2%
.781
33.3%
1362
 
15.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1562
66.7%
Other Punctuation781
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01200
76.8%
1362
 
23.2%
Other Punctuation
ValueCountFrequency (%)
.781
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01200
51.2%
.781
33.3%
1362
 
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01200
51.2%
.781
33.3%
1362
 
15.5%

contact_date
Date

HIGH CORRELATION

Distinct50
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size31.4 KiB
Minimum2018-01-03 00:00:00
Maximum2018-07-12 00:00:00
2022-04-14T10:23:11.201662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:23:11.312449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

cpi
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct23
Distinct (%)0.6%
Missing65
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean93.48446302
Minimum92.201
Maximum94.465
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2022-04-14T10:23:11.420081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.431
Q192.963
median93.444
Q393.994
95-th percentile94.465
Maximum94.465
Range2.264
Interquartile range (IQR)1.031

Descriptive statistics

Standard deviation0.612562774
Coefficient of variation (CV)0.006552562364
Kurtosis-0.9492400297
Mean93.48446302
Median Absolute Deviation (MAD)0.55
Skewness-0.1903496866
Sum367861.362
Variance0.3752331521
MonotonicityNot monotonic
2022-04-14T10:23:11.506020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
93.994559
14.0%
93.918546
13.7%
92.893520
13.0%
93.444411
10.3%
94.465354
8.8%
93.2304
7.6%
93.075290
7.2%
92.201134
 
3.4%
92.963114
 
2.9%
92.43182
 
2.1%
Other values (13)621
15.5%
ValueCountFrequency (%)
92.201134
 
3.4%
92.37945
 
1.1%
92.43182
 
2.1%
92.46936
 
0.9%
92.64967
 
1.7%
92.71331
 
0.8%
92.84350
 
1.2%
92.893520
13.0%
92.963114
 
2.9%
93.075290
7.2%
ValueCountFrequency (%)
94.465354
8.8%
94.21574
 
1.8%
94.19957
 
1.4%
94.05545
 
1.1%
94.02754
 
1.4%
93.994559
14.0%
93.918546
13.7%
93.87648
 
1.2%
93.79819
 
0.5%
93.74935
 
0.9%

subs_deposit
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.4 KiB
0
2410 
1
1590 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
02410
60.2%
11590
39.8%

Length

2022-04-14T10:23:11.674260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-14T10:23:11.732124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
02410
60.2%
11590
39.8%

Most occurring characters

ValueCountFrequency (%)
02410
60.2%
11590
39.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02410
60.2%
11590
39.8%

Most occurring scripts

ValueCountFrequency (%)
Common4000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02410
60.2%
11590
39.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII4000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02410
60.2%
11590
39.8%

contact_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.87125
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2022-04-14T10:23:11.775838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.006160414
Coefficient of variation (CV)0.5182203201
Kurtosis-1.259541467
Mean3.87125
Median Absolute Deviation (MAD)2
Skewness0.108145447
Sum15485
Variance4.024679607
MonotonicityNot monotonic
2022-04-14T10:23:11.845503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2719
18.0%
4599
15.0%
1587
14.7%
7546
13.7%
5528
13.2%
6513
12.8%
3508
12.7%
ValueCountFrequency (%)
1587
14.7%
2719
18.0%
3508
12.7%
4599
15.0%
5528
13.2%
6513
12.8%
7546
13.7%
ValueCountFrequency (%)
7546
13.7%
6513
12.8%
5528
13.2%
4599
15.0%
3508
12.7%
2719
18.0%
1587
14.7%

contact_day
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.69575
Minimum3
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2022-04-14T10:23:11.926030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q15
median6
Q38
95-th percentile11
Maximum12
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.163758859
Coefficient of variation (CV)0.3231540692
Kurtosis-0.3858109536
Mean6.69575
Median Absolute Deviation (MAD)1
Skewness0.6494946333
Sum26783
Variance4.681852401
MonotonicityNot monotonic
2022-04-14T10:23:11.997639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
51127
28.2%
7656
16.4%
8599
15.0%
6513
12.8%
11396
 
9.9%
4325
 
8.1%
10141
 
3.5%
3110
 
2.8%
9102
 
2.5%
1231
 
0.8%
ValueCountFrequency (%)
3110
 
2.8%
4325
 
8.1%
51127
28.2%
6513
12.8%
7656
16.4%
8599
15.0%
9102
 
2.5%
10141
 
3.5%
11396
 
9.9%
1231
 
0.8%
ValueCountFrequency (%)
1231
 
0.8%
11396
 
9.9%
10141
 
3.5%
9102
 
2.5%
8599
15.0%
7656
16.4%
6513
12.8%
51127
28.2%
4325
 
8.1%
3110
 
2.8%

contact_week
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.76075
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2022-04-14T10:23:12.074136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median14
Q323
95-th percentile27
Maximum28
Range27
Interquartile range (IQR)17

Descriptive statistics

Standard deviation8.62934678
Coefficient of variation (CV)0.6270985796
Kurtosis-1.246539989
Mean13.76075
Median Absolute Deviation (MAD)8
Skewness0.1200698391
Sum55043
Variance74.46562584
MonotonicityNot monotonic
2022-04-14T10:23:12.161876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
6633
15.8%
14534
13.4%
10461
11.5%
27442
11.1%
23392
9.8%
1356
8.9%
19293
7.3%
18235
 
5.9%
2231
 
5.8%
28104
 
2.6%
Other values (6)319
8.0%
ValueCountFrequency (%)
1356
8.9%
2231
 
5.8%
586
 
2.1%
6633
15.8%
935
 
0.9%
10461
11.5%
1112
 
0.3%
14534
13.4%
1565
 
1.6%
18235
 
5.9%
ValueCountFrequency (%)
28104
 
2.6%
27442
11.1%
2484
 
2.1%
23392
9.8%
2237
 
0.9%
19293
7.3%
18235
5.9%
1565
 
1.6%
14534
13.4%
1112
 
0.3%

contact_weekday
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8255
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2022-04-14T10:23:12.239803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.946534613
Coefficient of variation (CV)0.5088314241
Kurtosis-1.014293628
Mean3.8255
Median Absolute Deviation (MAD)1
Skewness-0.1061946803
Sum15302
Variance3.788996999
MonotonicityNot monotonic
2022-04-14T10:23:12.308726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1938
23.4%
4917
22.9%
5775
19.4%
3531
13.3%
7434
10.8%
6345
 
8.6%
260
 
1.5%
ValueCountFrequency (%)
1938
23.4%
260
 
1.5%
3531
13.3%
4917
22.9%
5775
19.4%
6345
 
8.6%
7434
10.8%
ValueCountFrequency (%)
7434
10.8%
6345
 
8.6%
5775
19.4%
4917
22.9%
3531
13.3%
260
 
1.5%
1938
23.4%

Interactions

2022-04-14T10:16:47.303313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:17:27.608856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:17:58.396545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:18:26.253031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:18:29.016130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:18:57.459720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:19:24.918398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:19:53.615075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:20:21.464141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:20:50.315411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:18.097641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:30.206305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:30.295369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:31.729947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:31.812223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:31.903463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:31.990658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:32.082264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:32.170690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:32.259962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:32.358731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:44.224187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:44.318454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:44.416112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:44.499810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:44.595952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:44.687168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:44.781759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:44.872461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:44.965110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:45.061185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:46.297174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:46.378584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:46.461829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:46.544159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:46.627185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:46.714572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:46.796539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:46.873987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:46.953527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:47.042175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:59.218531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:59.309925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:59.404094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:59.484864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:59.576883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:59.671381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:59.762585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:59.850270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:21:59.941585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:00.042101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:13.623005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:13.710955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:13.801715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:13.892984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:13.981242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:14.065136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:14.151718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:14.235281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:14.319890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:14.414396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:26.539742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:26.631458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:26.725200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:26.808294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:26.900025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:26.987056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:27.078984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:27.167805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:27.257648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:27.355555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:39.537006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:39.621989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:39.711356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:39.789722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:39.876446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:39.962933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:40.050862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:40.132099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:40.215845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:40.308593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:52.478834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:52.567788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:52.660290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:52.739721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:52.828695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:52.913477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:53.000923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:53.085754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:53.172454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:22:53.267718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:23:07.433498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:23:07.525887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:23:07.623847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:23:07.706463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:23:07.798222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:23:07.886313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:23:07.978535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:23:08.067895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:23:08.157826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-04-14T10:23:12.397149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-14T10:23:12.567321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-14T10:23:12.736958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-14T10:23:12.908382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-04-14T10:23:13.065587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-04-14T10:23:08.325371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-14T10:23:08.574121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-14T10:23:08.740242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-14T10:23:08.853197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

client_idage_bracketjobmaritaleducationhas_housing_loanhas_personal_loanprev_call_durationdays_since_last_callnum_contacts_prevpoutcomecontact_datecpisubs_depositcontact_monthcontact_daycontact_weekcontact_weekday
041020313.01.01.00.0283.03.011.02018-07-0992.379179281
123720453.02.00.01.0169.06.021.02018-05-0794.215157191
229378311.01.00.00.0552.0NaN0NaN2018-01-0893.44411821
336636222.03.01.01.0206.0NaN0NaN2018-02-1193.200021167
438229112.01.00.00.0341.0NaN0NaN2018-04-0493.075144143
527202231.02.00.00.081.0NaN0NaN2018-06-0893.444068235
61409411.01.00.00.01076.06.011.02018-07-0592.893175274
724379351.03.00.00.0133.0NaN0NaN2018-06-0793.918067234
810036271.02.00.00.0253.0NaN10.02018-03-0592.893035101
918115331.01.00.00.0467.0NaN0NaN2018-01-0694.46511616

Last rows

client_idage_bracketjobmaritaleducationhas_housing_loanhas_personal_loanprev_call_durationdays_since_last_callnum_contacts_prevpoutcomecontact_datecpisubs_depositcontact_monthcontact_daycontact_weekcontact_weekday
399024211271.03.01.00.0159.0NaN0NaN2018-06-0793.918067234
399128348341.02.00.00.077.0NaN0NaN2018-07-0893.444078277
39924739271.04.01.00.0418.0NaN0NaN2018-01-0592.89301515
39932389273.02.00.00.0827.0NaN0NaN2018-07-0592.893175274
399410248222.02.01.00.013.0NaN0NaN2018-03-0592.893035101
39957519362.02.01.00.0396.0NaN0NaN2018-02-0592.89312561
399629822311.01.01.00.0115.0NaN0NaN2018-01-0893.44401821
399724462211.03.01.00.0214.0NaN0NaN2018-06-0793.918067234
399826089241.02.01.00.076.0NaN0NaN2018-02-0793.91802763
399940631212.01.01.00.0368.0NaN0NaN2018-04-0992.379049151